Graphiti - An open-source AI dynamic knowledge graph generation framework
Graphiti is an AI knowledge graph generation framework designed for dynamic environments, enabling AI agents to build queryable, time-aware knowledge networks. Graphiti can ingest and process structured and unstructured data in real time...
What is Graphiti?
Graphiti is an AI knowledge graph generation framework designed for dynamic environments, enabling AI agents to build queryable, time-aware knowledge networks. Graphiti can ingest and process structured and unstructured data in real time, supporting dynamic data integration, state reasoning and automation, and hybrid retrieval. Graphiti is suitable for scenarios such as AI agent memory layers, real-time interactive systems, and enterprise knowledge management, providing agents with powerful dynamic memory and reasoning capabilities to help achieve smarter and more efficient AI applications.
Graphiti's main functions
- Dynamic data integrationIt can capture structured and unstructured data in real time and supports incremental updates.
- Time perception abilityTrack changes in facts and relationships over time, and support point-in-time queries.
- Hybrid SearchIt combines semantic search, keyword search, and graph traversal to achieve low-latency queries.
- State reasoning and automationSupports intelligent agents to automate tasks and perform reasoning based on graphs.
- ScalabilityDesigned for processing large-scale datasets and supporting parallel processing.
- Custom entity typeIt supports defining entity types for specific domains, enabling more accurate knowledge representation.
- Multimodal searchSupports complex queries based on semantics, keywords, and graph structures.
Graphiti's technical principles
- Time-aware graphAdd time-based metadata to the graph edges to record the lifecycle of relationships. Each relationship in the graph has a timestamp that tracks the entire process from creation to termination.
- Event-driven architectureBased on an event-driven architecture, data is ingested as discrete events. Each event includes the data source and timestamp, supporting incremental processing.
- Hybrid Search EngineThis approach combines semantic embedding, keyword search, and graph traversal techniques to achieve low-latency complex queries. Semantic embedding uses a vector space model to understand text content, keyword search quickly locates text fragments, and graph traversal uses graph structures for relational reasoning. Query results are reordered based on their distance from the central node, further optimizing the relevance of the results.
- ParallelizationGraphiti supports parallel processing, enabling it to handle multiple data streams simultaneously and improve data processing efficiency.
Graphiti project address
- Project official website:https://help.getzep.com/graphiti/graphiti
- GitHub repository:https://github.com/getzep/graphiti/
Graphiti application scenarios
- AI intelligent agent memory layerProvides long-term memory for AI assistants, supporting personalized services.
- Real-time interactive systemIt supports dynamic updates and historical queries, and is suitable for smart homes, smart customer service, etc.
- Enterprise knowledge managementIt enables efficient management of enterprise data and supports complex queries and decision support.
- HealthcareManage medical records and treatment information to assist medical AI in diagnosis and recommendations.
- Financial AnalysisReal-time market data updates support risk assessment and decision optimization.